Forced Out of Our Homelands

Exploring UNHCR Data on Forced Displacement to Predict Asylum Acceptance Rates

Maria Mohyuddin and Aarushi Tripathi

Exploring the Data

2022 has been one of the worst years

  • In 2022, millions were forcibly displaced by persecution, conflict, violence, human rights violations and events seriously disturbing public order

  • The numbers grew by 21% by the end of the year

  • More than 1 in 74 people worldwide remained forcibly displaced as a result

  • Almost 90 per cent of them in low- and middle-income countries

Top 10 Countries of Origin for Refugees

Important Definitions

Refugees include individuals granted complementary forms of protection, and those enjoying temporary protection.

Asylum-seekers are individuals who have sought international protection and whose claims for refugee status have not yet been determined.

Other people in need of international protection refers to people who are outside their country or territory of origin, typically because they have been forcibly displaced across international borders, who have not been reported under other categories but who likely need international protection.

Internally displaced persons are persons who have been forced or obliged to flee or to leave their homes , in particular as a result of, or in order to avoid the effects of armed conflict, situations of generalized violence, violations of human rights or natural or human-made disasters, and who have not crossed an internationally recognized State border.

In the last decade, 62.2 million refugees were added

Which Nations Do Displaced People Belong To?

# A tibble: 210 × 5
   coo_name            coo_iso total_refugees total_asylum total_idps
   <chr>               <chr>            <dbl>        <dbl>      <dbl>
 1 Afghanistan         AFG           31909074      2542213   20990275
 2 Albania             ALB             151217       227710          0
 3 Algeria             DZA              50977        71830          0
 4 Andorra             AND                 26           30          0
 5 Angola              AGO             115299        81448          0
 6 Anguilla            AIA                  5           15          0
 7 Antigua and Barbuda ATG                880          459          0
 8 Argentina           ARG               2166         7600          0
 9 Armenia             ARM             131910       107437          0
10 Aruba               ABW                  0            7          0
# ℹ 200 more rows

Refugee Population Across UNHCR Regions

Applications

Which Countries Accept Most Asylum Applications

Most Common Types of Applications

Predicting Acceptance Rates

Model Used and Design- Dependent and Independent Vars

::: panel-tabset

Random Forest Model

  • Dependent Variable: Acceptance Rate
  • Independent Variables: ISO Codes for Countries of Origin and Asylum, Procedure Type, Decision Level, Applicant Type

{# {r} # options(scipen = 999) # library(tidyverse) # library(modeldata) # library(tidymodels) # # # asy_dec_model <- asylum_decisions %>% # filter(dec_recognized + dec_other + dec_rejected + dec_closed == dec_total) %>% # mutate(acceptance_rate_pct = (dec_recognized + dec_other)/dec_total) # # forest_mod <- # rand_forest(mtry = 3, # trees = 500, # min_n = 4) %>% # set_engine(engine = "randomForest") %>% # set_mode("regression") # forest_mod # # set.seed(100) # Important! # # asylum_split <- initial_split(asy_dec) # asylum_train <- training(asylum_split) # asylum_test <- testing(asylum_split) # # rf_reg_fit <- forest_mod %>% # fit(acceptance_rate_pct ~ coo_iso + coa_iso + procedure_type + dec_level + dec_pc, # data = asylum_train) # rf_reg_fit #

Model results in terms of precision and recall

Run 2 simulations

Try to find weights given to each parameter